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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Roadmap to Adopting and Implementing MLOps in Organizations

M
Mayank Singh Last Updated : 10 Jan, 2023
5 min read

Introduction

Welcome to the world of MLOps, or Machine Learning Operations! If you’re an industry specialist looking to understand MLOps and how it can benefit your organization, then you’re at the right place.

MLOps, or Machine Learning Operations, is a set of practices and techniques that enables an organization to effectively build, deploy, and manage machine learning models in a production environment. It involves a combination of technical and non-technical considerations, including collaboration between data scientists and IT professionals, the model development and deployment process automation, and the establishment of governance and security protocols.

MLOps

One of the main challenges of MLOps is the integration of machine learning workflows into the overall software development lifecycle. This can be a complex process, as it involves establishing processes and tools for collaboration between data scientists and IT professionals and automating the model development and deployment process. However, the benefits of successful MLOps implementation can be significant, including improved model performance, faster time-to-market, and increased efficiency.

In addition to these technical considerations, MLOps also involves establishing governance and security protocols to ensure that machine learning models are used ethically and responsibly. This includes considerations such as bias, privacy, and data protection.

Overall, MLOps is essential for organizations looking to effectively deploy and manage machine learning models in a production environment. By establishing robust processes and tools for collaboration, automation, and governance, organizations can ensure that their machine-learning initiatives are successful and deliver value to their customers.

Use of MLOps in the Industry

The market for MLOps is rapidly growing as more organizations recognize the value of effectively deploying and managing machine learning models in a production environment.

One of the main drivers of this growth is the increasing adoption of machine-learning technologies across various industries. Machine learning solves complex problems and drives business value from healthcare and finance to retail and manufacturing. As a result, there is a growing demand for tools and practices that enable organizations to effectively build, deploy, and manage machine learning models in a production environment.

MLOps Explained - A Complete Introduction

Another factor contributing to the growth of the MLOps market is the increasing complexity of machine learning workflows. As organizations seek to build and deploy more advanced models, the need for robust processes and tools for collaboration, automation, and governance becomes more pressing. This has led to the developing of a range of MLOps platforms and tools, including open-source and commercial options, designed to support the end-to-end machine learning lifecycle.

Overall, the market for MLOps is expected to grow in the coming years as more organizations adopt machine learning technologies and seek to deploy and manage their models in a production environment effectively. This growth is expected to drive innovation, develop new platforms and tools, and evolve best practices and industry standards.

Benefits of Adopting MLOps in Organizations

MLOps offers a range of benefits to organizations across various industries. Some of the key benefits of MLOps include the following:

  1. Improved model performance: By establishing processes for continuous training and testing machine learning models, organizations can ensure that their models are always up-to-date and perform at their best.
  2. Faster time-to-market: Practices such as continuous integration and delivery of CI/CD pipelines enable organizations to quickly and easily deploy new models and updates, helping to speed up the time it takes to get new models into production.
  3. Increased efficiency: Automating the model development and deployment process can help organizations save time and resources, allowing data scientists to focus on more complex tasks while still delivering value to the business.
  4. Enhanced collaboration: MLOps practices such as version control and configuration management help to facilitate collaboration between data scientists and IT professionals, enabling organizations to take a more holistic approach to machine learning initiatives.
  5. Improved governance and security: MLOps also involves establishing protocols for governance and security, ensuring that machine learning models are used ethically and responsibly, and protecting sensitive data.

Adopting MLOps practices and tools can help organizations effectively deploy and manage machine learning models in a production environment, leading to improved model performance, faster time-to-market, increased efficiency, enhanced collaboration, and improved governance and security.

MLOps Tools Available in the Market

Several prominent tools in the MLOps world are commonly used to support the end-to-end machine learning lifecycle, including:

  1. Jenkins: Jenkins is an open-source automation server that helps organizations automate parts of the software development process. It is often used in MLOps to automate the model training and testing process.
  2. Ansible: Ansible is an open-source configuration management tool that helps organizations automate the configuration and deployment of applications and infrastructure. It is often used to automate the deployment and management of machine learning models.
  3. Git: Git is a version control system that helps organizations track and manage changes to their codebase. It is often used in MLOps to track changes to machine learning models and their dependencies.
  4. Docker: Docker is an open-source containerization platform that enables organizations to package applications and their dependencies into lightweight containers. It is often used in MLOps to containerize machine learning models and facilitate their deployment.
  5. Kubernetes: Kubernetes is an open-source container orchestration platform that helps organizations automate containerized applications’ deployment, scaling, and management. It is often used in MLOps to manage and scale machine learning models in a production environment.

These are just a few of the prominent tools in the MLOps world, and many others are available. The choice of tools will depend on an organization’s specific needs and requirements.

Future Insights: Adoption of MLOps

The future of MLOps, or Machine Learning Operations, looks bright as more organizations adopt machine learning technologies and seek to deploy and manage their models in a production environment effectively.

One key trend in the future of MLOps is the increasing adoption of cloud-based platforms and tools. Cloud computing offers a range of benefits for MLOps, including scalability, flexibility, and cost-efficiency. As a result, we expect to see a shift towards cloud-based MLOps platforms and tools in the coming years.

Another trend is the increasing focus on automation and machine learning model management. As machine learning models become more complex and organizations seek to build and deploy more advanced models, the need for robust tools and practices for model management will become more pressing. We can expect to see the development of new MLOps platforms and tools designed to support the end-to-end machine learning lifecycle, including model management and monitoring.

Overall, the future of MLOps looks bright as more organizations adopt machine learning technologies and seek to deploy and manage their models in a production environment effectively. We can expect to see continued innovation in the space, with new platforms and tools emerging to support the end-to-end machine learning lifecycle and help organizations drive business value through machine learning.

Conclusion

To summarize:

MLOps, or Machine Learning Operations, is a set of practices and techniques that enable organizations to effectively build, deploy, and manage machine learning models in a production environment. It involves a combination of technical and non-technical considerations, including collaboration between data scientists and IT professionals, the model development and deployment process automation, and establishing governance and security protocols.

MLOps uses tools and practices such as continuous integration and delivery (CI/CD) pipelines, version control and configuration management, and containerization to support the end-to-end machine learning lifecycle. Adopting MLOps practices and tools can help organizations improve model performance, increase efficiency, enhance collaboration, and ensure that machine learning models are used ethically and responsibly.

The market for MLOps is expected to grow as more organizations adopt machine learning technologies and seek to deploy and manage their models in a production environment effectively. This growth is expected to drive innovation, develop new MLOps platforms and tools, and evolve best practices and industry standards.

MLOpsMLOps Benefitsmlops in organizationMLOps pipeline
M
Mayank Singh

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Streamlining Machine Learning Workflows with MLOps
Streamlining Machine Learning Workflows with MLOps
Your Go-to Guide on Machine Learning Operations (MLOps)
Your Go-to Guide on Machine Learning Operations (MLOps)

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